Kai-ming Li

Micro-Doppler Signature Extraction and ISAR Imaging for Target With Micromotion Dynamics

The micromotion of a target will generate a micro-Doppler (m-D) effect in the frequency domain. The m-D effect is regarded as a unique property of the target, which has special significance in target detection, identification, and classification. The classical range-Doppler algorithm cannot obtain a clear inverse synthetic aperture radar (ISAR) image due to the m-D effect induced by micromotion dynamics. The m-D effect induced by periodical micromotion is represented as a sinusoidal modulation in a spectrogram, whereas the Doppler induced by a main body is depicted as the form of a straight line. Therefore, the extraction of an m-D signature is transformed into the separation of a sinusoid and a straight line. The cancellation technique is a classical method for removing ground clutter. Based on the same principle, the cancellation technique is applied to the spectrogram in this letter, which successfully achieves the separation of the m-D signature and gets the clearer ISAR image of the main body. The effectiveness and robustness of the algorithm are proved by simulation results.

A novel cognitive ISAR imaging method with random stepped frequency chirp signal

The random stepped frequency chirp signal (RSFCS) has better performance in anti-jamming than that of conventional stepped frequency chirp signal (SFCS). In combination with the theory of compressing sensing (CS), a novel ISAR imaging method is proposed based on RSFCS, in which the high resolution range profile (HRRP) is reconstructed by using the conventional OMP algorithm, whereas the cognitive approach is introduced to further reduce the number of sub-pulse in RSFCS. In the proposed method, via cognizing the characteristics of moving targets, the number of sub-pulse in each burst can be adjusted adaptively. Finally, in the cross-range direction, the accurate reconstruction of ISAR image by using CS theory is implemented, which can effectively accomplish unwrapping. With the proposed method, high quality HRRP and ISAR image can be achieved with fewer sub-pulses of RSFCS and lower burst repetition frequency (BRF). Some simulation results are given to validate the effectiveness and robustness of the proposed algorithm.

Wideband MIMO Radar Waveform Optimization Based on Dynamic Adjustment of Signal Bandwidth

Considering the need of multi-target imaging, a method about MIMO radar waveform optimization based on dynamic adjustment of signal bandwidth is proposed. At first, the closed-loop feedback between the range profile and the signal bandwidth is established, which can design the required bandwidth of transmit signal in different directions, according to the range profile of targets. And then, considering the request of beampattern and the bandwidth limitation, a waveform optimization model is established and solved. Therefore, the multi-target observation and the dynamic adjustment of the signal bandwidth are accomplished. What’s more, satisfactory imaging results are obtained under the least resource consumption. In the end, the simulation has proved the performance of the algorithm in low SNR circumstance.

Multi-Target Radar Imaging Based on Phased-MIMO Technique—Part II: Adaptive Resource Allocation

The advantages of phased-MIMO technique have been utilized for multi-target imaging in the first part of this paper, and the better multi-target imaging performance has been shown compared with the traditional phased-array radar imaging and traditional MIMO radar imaging. For multi-target imaging, the limited radar resources should be allocated for different targets to achieve the maximal performance of radar. By establishing and solving a radar resource allocation optimization model, an adaptive resource allocation strategy for multi-target imaging based on phased-MIMO technique is proposed. With the method, the multi-target imaging efficiency can be improved significantly. The effectiveness of the proposed method is demonstrated by simulations.

Multi-Target Radar Imaging Based on Phased-MIMO Technique—Part I: Imaging Algorithm

Radar imaging provides the shape structure information for target recognition. In this paper, a multi-target radar imaging method based on the emerging phased-MIMO (multiple-input multiple-output) technique is proposed. Each single-element transmit antenna in traditional MIMO radar imaging system is replaced by a transmit array (TA), which operates in phased-MIMO mode. In the phased-MIMO mode, each TA is divided into several sub-arrays operating in phased-array radar mode, and the transmitted waveform by each sub-array is orthogonal to each other. The sub-arrays steer different transmit beams to different targets’ directions with optimized waveform for the corresponding target. The virtual aperture produced by the waveform diversity is used for coherent processing to improve the signal-to-noise ratio and the signal-to-interference ratio, and then the single-snapshot imaging for multi-target with high azimuth resolution can be achieved based on dictionary optimization and orthogonal matching pursuit. Simulation results indicate the effectiveness of the proposed method.

SAR RAW data processing approach based on a combination of LBG algorithm and compressed sensing

Aimed at the problem of how to diminish SAR raw data apparently and realize SAR imaging effectively, a new approach for processing SAR raw data combined with Linde-Buzo-Gray (LBG) algorithm and Compressed Sensing (CS) is proposed in this paper. For SAR returned signals, CS is engaged to reduce the sampling number in the pulse duration, and LBG algorithm as a classical vector quantization (VQ) method, is employed to diminish encode number of every sample value. Next, data reconstruction process still contains the two ordinal steps according to LBG algorithm and CS theory, respectively. On the basis of that, the traditional SAR imaging method, Frequency Scaling (FS) algorithm, is carried out to achieve the final SAR image. Simulation results show that the high quality SAR image can be achieved on condition of the SAR raw data is diminished furthermore obviously, which is compared with the traditional method.